Graphical User Interface (gui) is the outer skin of programs that facilitate the interaction between the user and different type of computing devices. It is been used in different aspects ranging from normal computers...
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Graphical User Interface (gui) is the outer skin of programs that facilitate the interaction between the user and different type of computing devices. It is been used in different aspects ranging from normal computers, mobile device, to even very small device nowadays like watches. This interaction uses different tools and programming objects like images, text, buttons, checkboxes, etc. With this emergence of different types of guis, they become an essential component to be tested (if available in the software) to ensure that the software meets the required quality by the user. In contrast to non-functionaltesting, function testing of gui insures a proper interaction between the user and the application interface without dealing with the coding internals. In this paper, a strategy for gui functional testing using Simplified Swarm Optimization (SSO) is proposed. The SSO is used to generate an optimized test suite with the help of Event-Interaction Graph (EIG). The proposed strategy also manages and repairs the test suites by deleting the unnecessary event sequences that are not applicable. The proposed generation algorithm based on SSO has proved its effectiveness by evaluating it against other algorithms. In addition, the strategy is applied on a standard case study and proved its applicability in reality. Copyright (C) 2014, Karabuk University. Production and hosting by Elsevier B.V. All rights reserved.
guitesting ensures the software quality and user experience in the ever-changing mobile application development. Using test scripts is one of the main guitesting manner, but it might be obsolete when the gui changes...
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guitesting ensures the software quality and user experience in the ever-changing mobile application development. Using test scripts is one of the main guitesting manner, but it might be obsolete when the gui changes with the app's evolution. Current studies often rely on textual or visual similarity to perform test repair, but may be less effective when the interacted event sequence changes dramatically. In the interaction design, practitioners often provide multiple entry points to access the same function to gain higher openness and flexibility, which indicates that there may be multiple routes for reference in test repair. To evaluate the feasibility, we first conducted an exploratory study on 37 tests from 18 apps. The result showed that over 81% tests could be represented with alternative event paths, and using the extended paths could help enhance the test replay rate. Based on this finding, we propose a test-extension-based test repair algorithm named ExtRep. The method first uses test-extension to find alternative paths with similar test objectives based on feature coverage, and then finds repaired result with the help of sequence transduction probability proposed in NLP area. Experiments conducted on 40 popular applications demonstrate that ExtRep can achieve a success rate of 73.68% in repairing 97 tests, which significantly outperforms current approaches Water, Meter, and guider. Moreover, the test-extension approach displays immense potential for optimizing test repairs. A tool that implements the ExtRep is available for practical use and future research.
Graphical User Interface (gui) visualizes computer programs for the purpose of facilitating interaction between users and various computing devices. Today's computers, smart phones and even small devices such as w...
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ISBN:
(纸本)9781450354141
Graphical User Interface (gui) visualizes computer programs for the purpose of facilitating interaction between users and various computing devices. Today's computers, smart phones and even small devices such as watches are equipped with guis. Unlike command based interaction, gui uses images, labels, push buttons, radio buttons, etc. for the effective communication of users with a software system. guitesting is a critical part of software testing as it is the door to the actual functionality of software. For the quality assurance, gui functional testing of a software validates proper interaction between the interface and the user without considering any coding details. In this paper, a strategy based on fuzzy Adaptive Teaching Learning-based Optimization (ATLBO) algorithm, a variant of the basic Teaching Learning-based Optimization (TLBO) algorithm, for gui functional testing is proposed. ATLBO utilizes Event-Interaction Graph (EIG) for the generation of quality test cases. The proposed strategy has produced competitive experimental results against the basic TLBO and other test case generation algorithms.
Graphical User Interface(gui) visualizes computer programs for the purpose of facilitating interaction between users and various computing devices. Today's computers, smart phones and even small devices such as wa...
详细信息
Graphical User Interface(gui) visualizes computer programs for the purpose of facilitating interaction between users and various computing devices. Today's computers, smart phones and even small devices such as watches are equipped with guis. Unlike command based interaction, gui uses images, labels, push buttons, radio buttons, etc. for the effective communication of users with a software system. guitesting is a critical part of software testing as it is the door to the actual functionality of software. For the quality assurance, gui functional testing of a software validates proper interaction between the interface and the user without considering any coding details. In this paper, a strategy based on fuzzy Adaptive Teaching Learning-based Optimization(ATLBO) algorithm, a variant of the basic Teaching Learning-based Optimization(TLBO) algorithm, for gui functional testing is proposed. ATLBO utilizes Event-Interaction Graph(EIG) for the generation of quality test cases. The proposed strategy has produced competitive experimental results against the basic TLBO and other test case generation algorithms.
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